System and method for supplementing a question answering system with mixed-language source documents

ABSTRACT

Embodiments can provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a mixed-language question answering supplement system, the method comprising receiving a question in a target language; applying natural language processing to parse the question into at least one focus; for each focus, determining if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determining if one or more target language entities share direct syntactic dependency with the verb; determining one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determining whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity; if the dependency is similar, returning the source language entity as a member of a set; and if the set is full, returning an answer in the target language to the question in the target language.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under contract number2013-12101100008 awarded by United States defense agencies. Thegovernment has certain rights to this invention.

TECHNICAL FIELD

The present application relates generally to a system and method thatcan be used to supplement a question answering system withmixed-language source documents.

BACKGROUND

Mixed-language question answering systems sometimes lack sufficientinformation to properly answer a question due to data sparsity in thetarget language. However, the answer may be found in a corpus of adifferent language, which might be larger or otherwise better suited tothe domain. What is needed is a method for both identifying and scoringcandidate answers to questions using source documents from otherlanguages.

SUMMARY

Embodiments can provide a computer implemented method, in a dataprocessing system comprising a processor and a memory comprisinginstructions which are executed by the processor to cause the processorto implement a mixed-language question answering supplement system, themethod comprising receiving a question in a target language; applyingnatural language processing to parse the question into at least onefocus; for each focus, determining if one or more target language verbsshare direct syntactic dependency with the focus; for each of the one ormore verbs sharing direct syntactic dependency, determining if one ormore target language entities share direct syntactic dependency with theverb; determining one or more Abstract Universal Verbal Types associatedwith each verb; for each of the one or more Abstract Universal VerbalTypes, determining whether a dependency between a source language entityand a source language verb is of the same type as the dependency betweenthe target language verb and the target language entity; if thedependency is similar, returning the source language entity as a memberof a set; and if the set is full, returning an answer in the targetlanguage to the question in the target language.

Embodiments can further provide a method further comprising storing theone or more Abstract Universal Verbal Types in an ontology.

Embodiments can further provide a method further comprising receiving acorrespondence mapping of one or more named entities in the targetlanguage to one or more named entities in the source language.

Embodiments can further provide a method further comprising generatingthe correspondence mapping through natural language analysis of amixed-language corpus.

Embodiments can further provide a method further comprising assigning afunction from the target language entities to the source languageentities.

Embodiments can further provide a method further comprising assigning afunction from one or more surface-level verbal types to one or moreAbstract Universal Verbal Types.

Embodiments can further provide a method further comprising assigning afunction from the one or more Abstract Universal Verbal Types to one ormore verbal surface manifestations in the source language.

Embodiments can further provide a method further comprising assigning afunction from the one or more Abstract Universal Verbal Types to one ormore verbal surface manifestations in the target language.

In another illustrative embodiment, a computer program productcomprising a computer usable or readable medium having a computerreadable program is provided. The computer readable program, whenexecuted on a processor, causes the processor to perform various onesof, and combinations of, the operations outlined above with regard tothe method illustrative embodiment.

In yet another illustrative embodiment, a system is provided. The systemmay comprise a mixed-language question answering supplement processorconfigured to perform various ones of, and combinations of, theoperations outlined above with regard to the method illustrativeembodiment.

Additional features and advantages of this disclosure will be madeapparent from the following detailed description of illustrativeembodiments that proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of the present invention are bestunderstood from the following detailed description when read inconnection with the accompanying drawings. For the purpose ofillustrating the invention, there is shown in the drawings embodimentsthat are presently preferred, it being understood, however, that theinvention is not limited to the specific instrumentalities disclosed.Included in the drawings are the following Figures:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system implementing a mixed-language question answering (QA)supplement system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 illustrates a QA system pipeline, of a cognitive system, forprocessing an input question generated from the mixed-language QAsupplement system in accordance with one illustrative embodiment; and

FIG. 4 depicts a block diagram illustrating the functionality of amixed-language QA supplement system, according to embodiments describedherein.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The mixed-language question answering (QA) supplement system can work bymapping the named entities in the question (in the “target” language) tonamed entities in the passage (in the “source” language), and thenmapping the target language verbs that are connected to source languageverbs and other verbs in their hypernym/hyponym chain. The system canthen identify candidate answers that are connected to the sourcelanguage verb in the same manner as the target language verb is to itsfocus.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a head disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network(LAN), a wide area network (WAN), and/or a wireless network. The networkmay comprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computers,and/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including anobject-oriented programming language such as Java, Smalltalk, C++ or thelike, and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computer,or entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including LAN or WAN, or the connection may be made toan external computer (for example, through the Internet using anInternet Service Provider). In some embodiments, electronic circuitryincluding, for example, programmable logic circuitry, field-programmablegate arrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operations steps to be performed on the computer,other programmable apparatus, or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical functions. In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. IBMWatson™ is an example of one such cognitive system which can processhuman readable language and identify inferences between text passageswith human-like accuracy at speeds far faster than human beings and on amuch larger scale. In general, such cognitive systems are able toperform the following functions:

-   -   Navigate the complexities of human language and understanding    -   Ingest and process vast amounts of structured and unstructured        data    -   Generate and evaluate hypotheses    -   Weigh and evaluate responses that are based only on relevant        evidence    -   Provide situation-specific advice, insights, and guidance    -   Improve knowledge and learn with each iteration and interaction        through machine learning processes    -   Enable decision making at the point of impact (contextual        guidance)    -   Scale in proportion to the task    -   Extend and magnify human expertise and cognition    -   Identify resonating, human-like attributes and traits from        natural language    -   Deduce various language specific or agnostic attributes from        natural language    -   High degree of relevant recollection from data points (images,        text, voice) (memorization and recall)    -   Predict and sense with situation awareness that mimic human        cognition based on experiences    -   Answer questions based on natural language and specific evidence

In one aspect, cognitive systems provide mechanisms for answeringquestions posed to these cognitive systems using a Question Answeringpipeline or system (QA system). The QA pipeline or system is anartificial intelligence application executing on data processinghardware that answers questions pertaining to a given subject-matterdomain presented in natural language. The QA pipeline receives inputsfrom various sources including input over a network, a corpus ofelectronic documents or other data, data from a content creator,information from one or more content users, and other such inputs fromother possible sources of input, which can be in multiple languages.Data storage devices store the corpus of data. A content creator createscontent in a document for use as part of a corpus of data with the QApipeline. The document may include any file, text, article, or source ofdata for use in the QA system. For example, a QA pipeline accesses abody of knowledge about the domain, or subject matter area (e.g.,financial domain, medical domain, legal domain, etc.) where the body ofknowledge (knowledgebase) can be organized in a variety ofconfigurations, e.g., a structured repository of domain-specificinformation, such as ontologies, or unstructured data related to thedomain, or a collection of natural language documents about the domain.

Content users can input questions in a particular language to thecognitive system which implements the QA pipeline. The QA pipeline thenanswers the input questions using the content in the corpus or data byevaluating documents, sections of documents, portions of data in thecorpus, or the like. When a process evaluates a given section of adocument for semantic content, the process can use a variety ofconventions to query such document from the QA pipeline, e.g., sendingthe query to the QA pipeline as a well-formed question which is theninterpreted by the QA pipeline and a response is provided containing oneor more answers to the question. Semantic content is content based onthe relation between signifiers, such as words, phrases, signs, andsymbols, and what they stand for, their denotation, or connotation. Inother words, semantic content is content that interprets an expression,such as by using natural language processing.

As will be described in greater detail hereafter, the QA pipelinereceives an input question, parses the question to extract the majorfeatures of the question, uses the extracted features to formulatequeries, and then applies those queries to the corpus of data. Based onthe application of the queries to the corpus of data, the QA pipelinegenerates a set of hypotheses, or candidate answers to the inputquestion, by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question. The QA pipeline then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. There may behundreds or even thousands of reasoning algorithms applied, each ofwhich performs different analysis, e.g., comparisons, natural languageanalysis, lexical analysis, or the like, and generates a score. Forexample, some reasoning algorithms may look at the matching of terms andsynonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the QA pipeline. The statisticalmodel is used to summarize a level of confidence that the QA pipelinehas regarding the evidence that the potential response, i.e., candidateanswer, is inferred by the question. This process is repeated for eachof the candidate answers until the QA pipeline identifies candidateanswers that surface as being significantly stronger than others andthus generates a final answer, or ranked set of answers, for the inputquestion.

As mentioned above, QA pipeline and mechanisms operate by accessinginformation from a corpus of data or information (also referred to as acorpus of content), analyzing it, and then generating answer resultsbased on the analysis of this data. Accessing information from a corpusof data typically includes: a database query that answers questionsabout what is in a collection of structured records, and a search thatdelivers a collection of document links in response to a query against acollection of unstructured data (text, markup language, etc.).Conventional question answering systems are capable of generatinganswers based on the corpus of data and the input question, verifyinganswers to a collection of questions for the corpus of data, correctingerrors in digital text using a corpus of data, and selecting answers toquestions from a pool of potential answers, i.e., candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, determineuse cases for products, solutions, and services described in suchcontent before writing their content. Consequently, the content creatorsknow what questions the content is intended to answer in a particulartopic addressed by the content. Categorizing the questions, such as interms of roles, type of information, tasks, or the like, associated withthe question, in each document of a corpus of data allows the QApipeline to more quickly and efficiently identify documents containingcontent related to a specific query. The content may also answer otherquestions that the content creator did not contemplate that may beuseful to content users. The questions and answers may be verified bythe content creator to be contained in the content for a given document.These capabilities contribute to improved accuracy, system performance,machine learning, and confidence of the QA pipeline. Content creators,automated tools, or the like, annotate or otherwise generate metadatafor providing information useable by the QA pipeline to identifyquestion and answer attributes of the content.

Operating on such content, the QA pipeline generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.,candidate answers, for the input question. The most probable answers areoutput as a ranked listing of candidate answers ranked according totheir relative scores or confidence measures calculated duringevaluation of the candidate answers, as a single final answer having ahighest ranking score or confidence measure, or which is a best match tothe input question, or a combination of ranked listing and final answer.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system 100 implementing a question and answer (QA) pipeline108 in a computer network 102. One example of a question/answergeneration operation which may be used in conjunction with theprinciples described herein is described in U.S. Patent ApplicationPublication No. 2011/0125734, which is herein incorporated by referencein its entirety. The cognitive system 100 is implemented on one or morecomputing devices 104 (comprising one or more processors and one or morememories, and potentially any other computing device elements generallyknown in the art including buses, storage devices, communicationinterfaces, and the like) connected to the computer network 102. Thenetwork 102 includes multiple computing devices 104 in communicationwith each other and with other devices or components via one or morewired and/or wireless data communication links, where each communicationlink comprises one or more of wires, routers, switches, transmitters,receivers, or the like. The cognitive system 100 and network 102 enablesquestion/answer (QA) generation functionality for one or more cognitivesystem users via their respective computing devices. Other embodimentsof the cognitive system 100 may be used with components, systems,sub-systems, and/or devices other than those that are depicted herein.

The cognitive system 100 is configured to implement a QA pipeline 108that receive inputs from various sources. For example, the cognitivesystem 100 receives input from the network 102, corpora of electronicdocuments of multiple languages 140, cognitive system users, and/orother data and other possible sources of input. In one embodiment, someor all of the inputs to the cognitive system 100 are routed through thenetwork 102. The various computing devices 104 on the network 102include access points for content creators and QA system users. Some ofthe computing devices 104 include devices for a database storing thecorpora of data 140. Portions of the corpora of data 140 may also beprovided on one or more other network attached storage devices, in oneor more databases, or other computing devices not explicitly shown inFIG. 1. The network 102 includes local network connections and remoteconnections in various embodiments, such that the cognitive system 100may operate in environments of any size, including local and global,e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 140 for use as part of a corpus of data with thecognitive system 100. The document includes any file, text, article, orsource of data for use in the cognitive system 100. Mixed-language QAsupplement system users access the cognitive system 100 via a networkconnection or an Internet connection to the network 102, and inputquestions to the cognitive system 100 that are answered by the contentin the corpus of data 140. In one embodiment, the questions are formedusing natural language. The cognitive system 100 parses and interpretsthe question via a QA pipeline 108, and provides a response to thecognitive system user containing one or more answers to the question. Insome embodiments, the cognitive system 100 provides a response to usersin a ranked list of candidate answers while in other illustrativeembodiments, the cognitive system 100 provides a single final answer ora combination of a final answer and ranked listing of other candidateanswers.

The cognitive system 100 implements the QA pipeline 108 which comprisesa plurality of stages for processing an input question and the corpus ofdata 140. The QA pipeline 108 generates answers for the input questionbased on the processing of the input question and the corpus of data140. The QA pipeline 108 will be described in greater detail hereafterwith regard to FIG. 3.

In some illustrative embodiments, the cognitive system 100 may be theIBM Watson™ cognitive system available from International BusinessMachines Corporation of Armonk, N.Y., which is augmented with themechanisms of the illustrative embodiments described hereafter. Asoutlined previously, a QA pipeline of the IBM Watson™ cognitive systemreceives an input question, which it then parses to extract the majorfeatures of the question, and which in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question. TheQA pipeline of the IBM Watson™ cognitive system then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. The scoresobtained from the various reasoning algorithms are then weighted againsta statistical model that summarizes a level of confidence that the QApipeline of the IBM Watson™ cognitive system has regarding the evidencethat the potential response, i.e., candidate answer, is inferred by thequestion. This process is repeated for each of the candidate answers togenerate ranked listing of candidate answers which may then be presentedto the user that submitted the input question, or from which a finalanswer is selected and presented to the user. More information about theQA pipeline of the IBM Watson™ cognitive system may be obtained, forexample, from the IBM Corporation website, IBM Redbooks, and the like.For example, information about the QA pipeline of the IBM Watson™cognitive system can be found in Yuan et al., “Watson and Healthcare.”IBM developerWorks, 2011 and “The Era of Cognitive Systems: An InsideLook at IBM Watson and How it Works” by Rob High, IBM Redbooks, 2012.

As shown in FIG. 1, in accordance with some illustrative embodiments,the cognitive system 100 is further augmented, in accordance with themechanisms of the illustrative embodiments, to include logic implementedin specialized hardware, software executed on hardware, or anycombination of specialized hardware and software executed on hardware,for integrating a mixed-language question answering (QA) supplementsystem 120.

The mixed-language QA supplement system 120 can include a correspondencemapping module 121, which can contain a mapping of named entities in atarget language to corresponding named entities in a source language.For example, a correspondence mapping can be the “same-as” links inWikipedia, which can connect equivalent pages in different languages.The correspondence mapping can be generated through a natural languageanalysis of documents contained in a mixed-language corpus.Additionally, the mixed-language QA supplement system 120 can include anontology of Abstract Universal Verbal Types (AUVT) 123, which cancontain lexical manifestations in both the source and the targetexample. Abstract Universal Verbal Types can be verbal mechanisms thatcross language barriers, such that the verb is not changed in meaning,but merely in the language itself. Hypernym/hyponym relationships can bestored in the ontology 123. A hyponym is a word or phrase whose semanticfield is included within that of another word, which is its hypernym.For example, color is a hypernym of purple, red, blue, and green. Purpleis a hypernym of crimson, violet, and lavender.

One or more questions in a target language received from a user can besubjected to natural language processing techniques of the cognitivesystem 100 and/or mixed-language QA supplement system 120 to transformthe questions into acyclic graphs where nodes represent potential facts,and connectors represent the overall connections between the potentialfacts. This operation may be performed, for example, as part of aningestion operation of the cognitive system 100 which reads the naturallanguage text of the electronic documents or asked questions, parses thenatural language text and performs natural language processing on thenatural language text, including performing annotation operations usingannotators, to extract key features and facts of the natural languagetext which are then converted to the acyclic graphs. The reading,parsing, and extracting can occur independent of the language of theelectronic documents or asked questions.

The acyclic graphs of the analyzed QA pairs are stored in storage device150 associated with either the cognitive system 100 or themixed-language QA supplement system 120, where the storage device 150may be a memory, a hard disk based storage device, flash memory, solidstate storage device, or the like (hereafter assumed to be a “memory”with in-memory representations of the acyclic graphs for purposes ofdescription). The in-memory acyclic graphs are then analyzed by thereusable branch engine 122 of the mixed-language QA supplement system120 to identify reusable branches within the acyclic graphs and areusable branch data structure having entries for each reusable branchfound in this way, and other reusable branches either found in othercorpora, readily known and pre-populated in the reusable branch datastructure by subject matter experts, or the like, is generated, asdescribed further in FIG. 4. The identification of the reusable branchesmay further be associated with the in-memory acyclic graph of thecorresponding question as well so as to identify for the particularknowledge domain what the reusable branches are in the knowledge domain.

Either as part of an ingestion operation, or by the QA acyclic graphanalysis engine 124 analyzing the acyclic graphs generated by theingestion operation, a QA element data structure 126 defining thevarious knowledge domains in the ingested corpus 140, as well as otherQA elements pre-populated in the QA element data structure 126 eitherthrough analysis of other corpora or through manual input by subjectmatter expert interaction, is generated.

The QA element data structure 126 is analyzed by clustering engine 128to identify clusters of QA elements based on their characteristics. Forexample, QA elements may be clustered according to similar QA elementtypes to form QA element clusters. These clusters may be stored in acluster data structure 129. As noted above, some QA elements may havesub-elements and various levels of clustering may be performed, whichmay be classified/clustered into other clusters. Thus, the same QAelement may be present in multiple clusters.

In response to receiving the input utterance, a similar QA elementsearch engine 130 performs a search of the cluster data structure 129 togenerate a listing of the QA element clusters that involve the given newQA element(s). In making this list, the cluster search engine 130 mayanalyze the clusters of reusable branches that contain the new QAelement to produce an initial list of candidate QA elements. Thislisting is then extended with candidate QA elements for similar QAelements obtained from clusters with which the elements of the reusablebranches involving the new QA element are clustered.

Alternatively, the clustering performed by the clustering engine 128 maybe performed after the identification of similar QA elements to those ofthe reusable branches found as having the new QA element(s), performedby the similar QA element search engine 130 and the list may then beextended with candidate QA elements for similar QA elements by using aprovided QA ontology data structure 132. Those candidate elements may beincluded in the listing and the listing may be analyzed by theclustering engine 128 to generate clusters of QA elements for storage inthe cluster data structure 129.

In either case, the similar QA element search engine 130 then determineswhether the presented question already contains any of the clusters ofcandidate QA elements, i.e., the clusters identified as having the newQA element(s) in the request. For those that are already present withinthe utterance, the candidate clusters may be promoted in the listing togenerate a filtered listing of candidate QA elements and their clusters134.

The QA element clusters in the filtered listing of candidate QA elements134 are then analyzed by a QA element compatibility engine 136 toidentify which of the element clusters are compatible with the knowledgedomain of the question that is to be answered. The QA elementcompatibility engine 136 may utilize configured association ruleslearned during a training of the mixed-language QA supplement system 120and knowledge base, where the association rules specify compatibility ofQA elements with different knowledge domains. Using these associationrules, the QA element compatibility engine determines what combinationsor patterns of one or more QA elements are found in questions asked bysubject matter experts working in the same knowledge domain. Theintersection of the association rules with the candidate QA elementclusters indicates which element clusters are compatible with theknowledge domain. The resulting candidate clusters that intersect withthe association rules may then be ranked by the QA element compatibilityengine 136, such as based on frequency of appearance of the clusters orQA elements in the clusters. Other ranking criteria may also be utilizedas noted above.

A QA element cluster in the filtered listing of candidate clusters 134,which also intersects with one or more of the association rules, isselected by the QA element compatibility engine 136 for use in providingan answer to a question. This selection may be based on the ranking ofthe clusters intersecting the association rules as discussed above. Forexample, a top ranked cluster may be selected for use in presenting ananswer to the utterance. Alternatively, other selection criteria may beutilized as well, such as in an implementation where ranking of theclusters may not be performed, as previously discussed above.

FIG. 2 is a block diagram of an example data processing system 200 inwhich aspects of the illustrative embodiments are implemented. Dataprocessing system 200 is an example of a computer, such as a server orclient, in which computer usable code or instructions implementing theprocess for illustrative embodiments of the present invention arelocated. In one embodiment, FIG. 2 represents a server computing device,such as a server, which implements the mixed-language QA supplementsystem 120 and cognitive system 100 described herein.

In the depicted example, data processing system 200 can employ a hubarchitecture including a north bridge and memory controller hub (NB/MCH)201 and south bridge and input/output (I/O) controller hub (SB/ICH) 202.Processing unit 203, main memory 204, and graphics processor 205 can beconnected to the NB/MCH 201. Graphics processor 205 can be connected tothe NB/MCH through an accelerated graphics port (AGP).

In the depicted example, the network adapter 206 connects to the SB/ICH202. The audio adapter 207, keyboard and mouse adapter 208, modem 209,read only memory (ROM) 210, hard disk drive (HDD) 211, optical drive (CDor DVD) 212, universal serial bus (USB) ports and other communicationports 213, and the PCI/PCIe devices 214 can connect to the SB/ICH 202through bus system 216. PCI/PCIe devices 214 may include Ethernetadapters, add-in cards, and PC cards for notebook computers. ROM 210 maybe, for example, a flash basic input/output system (BIOS). The HDD 211and optical drive 212 can use an integrated drive electronics (IDE) orserial advanced technology attachment (SATA) interface. The super I/O(SIO) device 215 can be connected to the SB/ICH.

An operating system can run on processing unit 203. The operating systemcan coordinate and provide control of various components within the dataprocessing system 200. As a client, the operating system can be acommercially available operating system. An object-oriented programmingsystem, such as the Java™ programming system, may run in conjunctionwith the operating system and provide calls to the operating system fromthe object-oriented programs or applications executing on the dataprocessing system 200. As a server, the data processing system 200 canbe an IBM® eServer System p® running the Advanced Interactive Executiveoperating system or the Linux operating system. The data processingsystem 200 can be a symmetric multiprocessor (SMP) system that caninclude a plurality of processors in the processing unit 203.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as the HDD 211, and are loaded into the main memory 204 forexecution by the processing unit 203. The processes for embodiments ofthe mixed-language QA supplement system can be performed by theprocessing unit 203 using computer usable program code, which can belocated in a memory such as, for example, main memory 204, ROM 210, orin one or more peripheral devices.

A bus system 216 can be comprised of one or more busses. The bus system216 can be implemented using any type of communication fabric orarchitecture that can provide for a transfer of data between differentcomponents or devices attached to the fabric or architecture. Acommunication unit such as the modem 209 or network adapter 206 caninclude one or more devices that can be used to transmit and receivedata.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIG. 2 may vary depending on the implementation. Otherinternal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives may be used inaddition to or in place of the hardware depicted. Moreover, the dataprocessing system 200 can take the form of any of a number of differentdata processing systems, including but not limited to, client computingdevices, server computing devices, tablet computers, laptop computers,telephone or other communication devices, personal digital assistants,and the like. Essentially, data processing system 200 can be any knownor later developed data processing system without architecturallimitation.

FIG. 3 illustrates a QA system pipeline, of a cognitive system, forprocessing an input utterance in accordance with one illustrativeembodiment. The QA system pipeline of FIG. 3 may be implemented, forexample, as QA pipeline 108 of cognitive system 100 in FIG. 1. It shouldbe appreciated that the stages of the QA pipeline shown in FIG. 3 areimplemented as one or more software engines, components, or the like,which are configured with logic for implementing the functionalityattributed to the particular stage. Each stage is implemented using oneor more of such software engines, components, or the like. The softwareengines, components, etc., are executed on one or more processors of oneor more data processing systems or devices and utilize or operate ondata stored in one or more data storage devices, memories, or the like,on one or more of the data processing systems. The QA pipeline of FIG. 3is augmented, for example, in one or more of the stages to implement themixed-language QA supplement 120 mechanism of the illustrativeembodiments described hereafter, additional stages may be provided toimplement the improved mechanism, or separate logic from the pipeline108 may be provided for interfacing with the pipeline 108 andimplementing the improved functionality and operations of theillustrative embodiments.

As shown in FIG. 3, the QA pipeline 108 comprises a plurality of stages310-380 through which the cognitive system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 310, the QA pipeline 108 receives an input question that ispresented in a natural language format. That is, a user inputs, via auser interface, an input question for which the user wishes to obtain ananswer, e.g., “Who were Washington's closest advisors?” In response toreceiving the input question, the next stage of the QA pipeline 108,i.e., the question and topic analysis stage 320, parses the inputquestion using natural language processing (NLP) techniques to extractmajor features (also referred to as foci) from the input question, andclassify the major features according to types, e.g., names, dates, orany of a plethora of other defined topics. For example, in the examplequestion above, the term “who” may be associated with a topic for“persons” indicating that the identity of a person is being sought,“Washington” may be identified as a proper name of a person with whichthe question is associated, “closest” may be identified as a wordindicative of proximity or relationship, and “advisors” may beindicative of a noun or other language topic.

In addition, the extracted major features include key words and phrasesclassified into question characteristics, such as the focus of thequestion, the lexical answer type (LAT) of the question, and the like.As referenced to herein, a lexical answer type (LAT) is a word in, or aword inferred from, the input question that indicates the type of theanswer, independent of assigning semantics to that word. For example, inthe question “What maneuver was invented in the 1500s to speed up thegame and involves two pieces of the same color?” the LAT is the string“maneuver.” The focus of a question is the part of the question that, ifreplaced by the answer, makes the question a standalone statement. Forexample, in the question “What drug has been shown to relieve thesymptoms of ADD with relatively few side effects?,” the focus is “drug”since if this word were replaced with the answer, e.g., “Adderall,” theanswer can be used to replace the term “drug” to generate the sentence“Adderall has been shown to relieve the symptoms of ADD with relativelyfew side effects.” The focus often, but not always, contains the LAT. Onthe other hand, in many cases, it is not possible to infer a meaningfulLAT from the focus.

Referring again to FIG. 3, the identified major features are then usedduring the question decomposition stage 330 to decompose the questioninto one or more queries that are applied to the mixed-language corporaof data/information 345 in order to generate one or more hypotheses. Thequeries are generated in any known or later developed query language,such as the Structure Query Language (SQL), or the like. The queries areapplied to one or more databases storing information about theelectronic texts, documents, articles, websites, and the like, that makeup the mixed-language corpora of data/information 345. That is, thesevarious sources themselves, different collections of sources, and thelike, represent a different corpus 347 within the mixed-language corpora345. There may be different corpora 345 defined for differentcollections of documents based on various criteria depending upon theparticular implementation. For example, different corpora may beestablished for different topics, subject matter categories, sources ofinformation, or the like. As one example, a first corpus may beassociated with healthcare documents while a second corpus may beassociated with financial documents. Alternatively, one corpus may bedocuments published by the U.S. Department of Energy while anothercorpus may be IBM Redbooks documents. Any collection of content havingsome similar attribute may be considered to be a corpus 347 within themixed-language corpora 345.

The queries are applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data140 in FIG. 1. The queries are applied to the corpus of data/informationat the hypothesis generation stage 340 to generate results identifyingpotential hypotheses for answering the input question, which can then beevaluated. That is, the application of the queries results in theextraction of portions of the corpus of data/information matching thecriteria of the particular query. These portions of the corpus are thenanalyzed and used, during the hypothesis generation stage 340, togenerate hypotheses for answering the input question. These hypothesesare also referred to herein as “candidate answers” for the inputquestion. For any input question, at this stage 340, there may behundreds of hypotheses or candidate answers generated that may need tobe evaluated.

The QA pipeline 108, in stage 350, then performs a deep analysis andcomparison of the language of the input question and the language ofeach hypothesis or “candidate answer,” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As described in FIG. 1, thisinvolves using a plurality of reasoning algorithms, each performing aseparate type of analysis of the language of the input question and/orcontent of the corpus that provides evidence in support of, or not insupport of, the hypothesis. Each reasoning algorithm generates a scorebased on the analysis it performs which indicates a measure of relevanceof the individual portions of the corpus of data/information extractedby application of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e., a measure of confidence in thehypothesis. There are various ways of generating such scores dependingupon the particular analysis being performed. In general, however, thesealgorithms look for particular terms, phrases, or patterns of text thatare indicative of terms, phrases, or patterns of interest and determinea degree of matching with higher degrees of matching being givenrelatively higher scores than lower degrees of matching.

In the synthesis stage 360, the large number of scores generated by thevarious reasoning algorithms are synthesized into confidence scores orconfidence measures for the various hypotheses. This process involvesapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QApipeline 108 and/or dynamically updated. For example, the weights forscores generated by algorithms that identify exactly matching terms andsynonyms may be set relatively higher than other algorithms that areevaluating publication dates for evidence passages. The weightsthemselves may be specified by subject matter experts or learned throughmachine learning processes that evaluate the significance ofcharacteristics evidence passages and their relative importance tooverall candidate answer generation.

The weighted scores are processed in accordance with a statistical modelgenerated through training of the QA pipeline 108 that identifies amanner by which these scores may be combined to generate a confidencescore or measure for the individual hypotheses or candidate answers.This confidence score or measure summarizes the level of confidence thatthe QA pipeline 108 has about the evidence that the candidate answer isinferred by the input question, i.e., that the candidate answer is thecorrect answer for the input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 370 which compares the confidencescores and measures to each other, compares them against predeterminedthresholds, or performs any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe correct answer to the input question. The hypotheses/candidateanswers are ranked according to these comparisons to generate a rankedlisting of hypotheses/candidate answers (hereafter simply referred to as“candidate answers”). From the ranked listing of candidate answers, atstage 380, a final answer and confidence score, or final set ofcandidate answers and confidence scores, are generated and output to thesubmitter of the original input question via a graphical user interfaceor other mechanism for outputting information.

However, in some instances the QA pipeline 108 may not be able to answera particular question posed in a target language due to limitation withthat target language's source corpora. In that instance, themixed-language QA supplement system 120 can interact with the QApipeline 108 in order to support the pipeline and provide an answer thatmay be found in a mixed-language corpora 345 containing source materialin a language different than that of the input question.

FIG. 4 depicts a block diagram illustrating the functionality of amixed-language QA supplement system, according to embodiments describedherein. First, the system can assign a function from the target languageentities to the source language entities 401. The system can then assigna function from surface-level verbal types (for both the source andtarget languages) to one or more Abstract Universal Verbal Types 402.The system can assign a function from the one or more Abstract UniversalVerbal Types to one or more verbal surface manifestations in the sourcelanguage 403. The system can then assign a function from the one or moreAbstract Universal Verbal Types to one or more verbal surfacemanifestations in the target language 404.

As described above, the mixed-language QA supplement system can receivea question Q in a target language 405. From the question Q, the systemcan parse at least one focus using natural language processing 406. Foreach focus, the system can determine if one or more verbs share a directsyntactic dependency with the focus 407. Direct syntactic dependency canmean that the verb directly depends on the subject, which in this caseis the particular focus, and is determined based on the predicate andtheir arguments. For each of the one or more verbs that share directsyntactic dependency, the system can determine if one or more entitiesin the target language share direct syntactic dependency with theparticular verb 408. Using the functions assigned above, the system canthen determine what Abstract Universal Verbal Types are associated witheach verb 409.

The system can then determine whether there exists a core dependencybetween a source language entity and a source language verb (both takenfrom a source language passage), and whether that core dependency is ofthe same type as the dependency between the target language verb and thetarget language entity (which are derived from the target languagequestion) 410. If the core dependencies are similar, the system canreturn the source language entity as a member of a set 411. Afterrepeating steps 410 and 411 for each AUVT, steps 409, 410, and 411 foreach entity, steps 408, 409, 410, and 411 for each verb, and steps 407,408, 409, 410, and 411 for each focus in the question, the system willhave populated a full set of returned source language entities.

The system steps described above can also be described in algorithmicformat:

1. Let m(x) be a function from target language entities to sourcelanguage entities; 2. Let a(x) be a function from surface-level verbaltypes (source and target language) to Abstract Universal Verbal Types;3. Let s(x) be a function from Abstract Universal Verbal Types to verbalsurface manifestations in the source language; 4. Let t(x) be a functionfrom Abstract Universal Verbal Types to verbal surface manifestations inthe target language; 5. Given a question Q, and a passage P, and anempty set R; 6. For each focus F in Q: 7.   For each verb V with adirect syntactic dependency with F: 8.      For each entity E with adirect syntactic dependency with V: 9.         For each AbstractUniversal Verbal Type A that is associated with V: 10.            Foreach source language verb x = a(A): 11.               Let e = m(E);12.               If there is a Core Dependency between e and x of thesame type as the dependency between V and E: 13.                 Let r =the entity connected to x by the same dependency between V and thefocus; 14.                 Add r to R 15. Return R

The functionality of the mixed-language QA supplement system up untilstage 411 can be beneficially described through example. In an example,the QA pipeline may be presented with a question in a target language ofArabic, such as “

” which, when translated, means, “Anna Harrison was buried at whatlocation?” However, this question about the wife of the 9^(th) presidentof the United States cannot be answered using standard Arab sources,such as the Arabic Wikipedia. At present, the Wikipedia page in Arabicfor Anna Harrison is a stub—only one sentence long. In contrast, theEnglish Wikipedia page for Anna Harrison is longer, and containsinformation that answers the operant question: “Anna Harrison died onFeb. 25, 1864, at age 88, and was buried at the William Henry HarrisonTomb State Memorial in North Bend.”

In the question, there is a syntactic/semantic relation between the verb

(bury) and the named entity

(Anna Harrison). The named entity

can be mapped to “Anna Harrison” using a correspondence mapping of namedentities 121 in the target language to corresponding named entities inthe source language. For instance, a manifestation of the correspondencemapping of entities 121 can be the “same-as” links in Wikipedia, whichcan connect equivalent pages in different languages. The verb “bury” ismapped to the verbs “

,

” Presented with the English source language passage above, the systemcan detect that the syntactic relationship between “bury” and “AnnaHarrison” in the source language passage is the same as the syntacticrelationship between “

” and “

” in the target language question. This tells us that this passage isrelevant to the question. The system can also detect that the syntacticrelation between “

” in the question and the focus is present in the passage. Followingthat relation in the passage can lead the system to the phrase “WilliamHenry Harrison Tomb State Memorial.” This phrase can then be used togenerate the corresponding Arabic phrase, or score it according to thepassage 415.

The system may identify one or more parallel passages using the set ofreturned source language entities 412. A parallel passage can be apassage wherein all of the core arguments of the verb (excluding,perhaps, the focus argument) are matched. The system can then proceed toidentify the presence or absence of oblique nominal arguments 413.Oblique nominal arguments can be verbal arguments that are less stronglyconnected to the verb, and which can manifest in looser ways, especiallycross-lingually. Oblique nominal arguments can appear inside asubordinate clause, or even in an entirely different sentence, based onthe grammatical norms of the target and source languages.

For example, in the question “Who was elected Prime Minister in Canadain 2008?,” the system can first establish a positive match on the verband the single core argument (“prime minister”), and then can look morebroadly for the presence or absence of equivalent terms to “Canada” and“2008” elsewhere in the passage/title. In identifying oblique nominalarguments, the system can be tolerant of weaker semantic relations, suchas to account for sentences like “In the year of 2006, the people ofCanada elected Stephen Harper as Prime Minister,” where the key obliquearguments (“2006” and “Canada”) are only weakly related to the verb. Thesystem can also allow for oblique nominal arguments to be represented inthe title of the source language passage (e.g., “Elections in Canada”,“People Elected in 2006”). The system can measure the precision of theoblique nominal arguments in the parallel passages against those presentin the target language question 414. The system can then return ananswer to the target question in the target language based on a scoringof the parallel passages based on the accuracy of their oblique nominalarguments 415. In an embodiment, the scoring can be based on alogarithmic scale.

As another example of the system considering oblique nominal arguments,consider the question in a target language of Spanish: “Quien fueelegido presidente en los estados unidos en el año dos mil?” (“Who waselected President of the United States in the year 2000?”). The system,through performing the steps, may identify the following source languagepassages as parallel passages: 1) a passage in English stating that“George W. Bush was elected president after a lengthy supreme courtbattle,” with the passage title “United States Presidential Election2000;” 2) an Arabic passage that translates to “The people electedBarack Obama President of the United States in 2008;” and 3) a Spanishpassage that translates to “Clinton was elected in 1992, defeatingGeorge H. W. Bush.”

Each of the source language parallel passages satisfies the corecriterion of equivalent core arguments (i.e., the verbs align to thesame meaning, and the core argument (“presidente”) from the targetlanguage question is present in all three passages. Thus, the system canconsider the three passages as parallel passages. The system can thenignore the core argument slot corresponding to the focus in the targetlanguage question and the several candidate answers in the passage, andcan focus on the nominal modifiers. The target language question has twoadjunct modifiers “Estados Unidos” and “dos mil.” The system can map theadjunct modifiers to their equivalents in the source languages ofEnglish and Arabic. The system can analyze the parallel passages and candetermine that passage 1 contains both of the modifiers in the title ofthe document, passage 2 contains only one of the modifiers in thepassage, and passage 3 contains none of the modifiers. The system canthen score the parallel passages according to the presence or lack ofthe modifiers, with passage 1 receiving the highest score, passage 3 thelowest score, and passage 2 receiving a score between the scores ofpassages 1 and 3. The system can then translate and display an answer tothe target language question based upon the information derived frompassage 1.

The system and processes of the figures are not exclusive. Othersystems, processes and menus may be derived in accordance with theprinciples of embodiments described herein to accomplish the sameobjectives. It is to be understood that the embodiments and variationsshown and described herein are for illustration purposes only.Modifications to the current design may be implemented by those skilledin the art, without departing from the scope of the embodiments. Asdescribed herein, the various systems, subsystems, agents, managers, andprocesses can be implemented using hardware components, softwarecomponents, and/or combinations thereof. No claim element herein is tobe construed under the provisions of 35 U.S.C. 112, sixth paragraph,unless the element is expressly recited using the phrase “means for.”

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of,” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within in thescope of the description and claims.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples are intendedto be non-limiting and are not exhaustive of the various possibilitiesfor implementing the mechanisms of the illustrative embodiments. It willbe apparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the example provided herein without departing from thespirit and scope of the present invention.

Although the invention has been described with reference to exemplaryembodiments, it is not limited thereto. Those skilled in the art willappreciate that numerous changes and modifications may be made to thepreferred embodiments of the invention and that such changes andmodifications may be made without departing from the true spirit of theinvention. It is therefore intended that the appended claims beconstrued to cover all such equivalent variations as fall within thetrue spirit and scope of the invention.

1. A computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a mixed-language question answering supplement system, the method comprising: receiving a question in a target language; applying natural language processing to parse the question into at least one focus; for each focus, determining if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determining if one or more target language entities share direct syntactic dependency with the verb; determining one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determining whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity comprising: using a cognitive system to generate a plurality of reasoning algorithms to analyze the source language and the target language, wherein each reasoning algorithm generates a dependency score; training a statistical model employed by a question answer pipeline; applying the trained statistical model to determine a weight for each dependency score; applying the weight to each dependency score to generate weighted dependency scores; and processing the weighted dependency scores with the statistical model to generate one or more confidence scores measuring dependency; if the dependency is similar, returning the source language entity as a member of a set; and if the set is full, returning an answer in the target language to the question in the target language.
 2. The method as recited in claim 1, further comprising: storing the one or more Abstract Universal Verbal Types in an ontology.
 3. The method as recited in claim 1, further comprising: receiving a correspondence mapping of one or more named entities in the target language to one or more named entities in the source language.
 4. The method as recited in claim 3, further comprising: generating the correspondence mapping through natural language analysis of a mixed-language corpus.
 5. The method as recited in claim 1, further comprising: assigning a function from the target language entities to the source language entities.
 6. The method as recited in claim 1, further comprising: assigning a function from one or more surface-level verbal types to one or more Abstract Universal Verbal Types.
 7. The method as recited in claim 1, further comprising: assigning a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the source language.
 8. The method as recited in claim 1, further comprising: assigning a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the target language.
 9. A computer program product for question and answer generation, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: receive a question in a target language; apply natural language processing to parse the question into at least one focus; for each focus, determine if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determine if one or more target language entities share direct syntactic dependency with the verb; determine one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determine whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity comprising: use a cognitive system to generate a plurality of reasoning algorithms to analyze the source language and the target language, wherein each reasoning algorithm generates a dependency score; train a statistical model employed by a question answer pipeline; apply the trained statistical model to determine a weight for each dependency score; apply the weight to each dependency score to generate weighted dependency scores; and process the weighted dependency scores with the statistical model to generate one or more confidence scores measuring dependency; if the dependency is similar, return the source language entity as a member of a set; and if the set is full, return an answer in the target language to the question in the target language.
 10. The computer program product as recited in claim 9, wherein the processor is further caused to: store the one or more Abstract Universal Verbal Types in an ontology.
 11. The computer program product as recited in claim 9, wherein the processor is further caused to: receive a correspondence mapping of one or more named entities in the target language to one or more named entities in the source language.
 12. The computer program product as recited in claim 11, wherein the processor is further caused to: generate the correspondence mapping through natural language analysis of a mixed-language corpus.
 13. The computer program product as recited in claim 9, wherein the processor is further caused to: assign a function from the target language entities to the source language entities.
 14. The computer program product as recited in claim 9, wherein the processor is further caused to: assign a function from one or more surface-level verbal types to one or more Abstract Universal Verbal Types.
 15. The computer program product as recited in claim 9, wherein the processor is further caused to: assign a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the source language.
 16. The computer program product as recited in claim 9, wherein the processor is further caused to: assign a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the target language.
 17. A mixed-language question answering supplement system, comprising: a mixed-language question answering supplement processor configured to: receive a question in a target language; apply natural language processing to parse the question into at least one focus; for each focus, determine if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determine if one or more target language entities share direct syntactic dependency with the verb; determine one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determine whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity comprising: use a cognitive system to generate a plurality of reasoning algorithms to analyze the source language and the target language, wherein each reasoning algorithm generates a dependency score; train a statistical model employed by a question answer pipeline; apply the trained statistical model to determine a weight for each dependency score; apply the weight to each dependency score to generate weighted dependency scores; and processing the weighted dependency scores with the statistical model to generate one or more confidence scores measuring dependency; if the dependency is similar, return the source language entity as a member of a set; and if the set is full, return an answer in the target language to the question in the target language.
 18. The system as recited in claim 17, wherein the mixed-language question answering supplement processor is further configured to: receive a correspondence mapping of one or more named entities in the target language to one or more named entities in the source language.
 19. The system as recited in claim 18, wherein the mixed-language question answering supplement processor is further configured to: generate the correspondence mapping through natural language analysis of a mixed-language corpus.
 20. The system as recited in claim 17, wherein the mixed-language question answering supplement processor is further configured to: assign a function from the target language entities to the source language entities; assign a function from one or more surface-level verbal types to one or more Abstract Universal Verbal Types; assign a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the source language; and assign a function from the one or more Abstract Universal Verbal Types to one or more verbal surface manifestations in the target language. 